class_docstr: FPN structure in NASFPN.
  
  Implementation of paper `NAS-FCOS: Fast Neural Architecture Search for
  Object Detection <https://arxiv.org/abs/1906.04423>`_
  
  Args:
      in_channels (List[int]): Number of input channels per scale.
      out_channels (int): Number of output channels (used at each scale)
      num_outs (int): Number of output scales.
      start_level (int): Index of the start input backbone level used to
          build the feature pyramid. Default: 0.
      end_level (int): Index of the end input backbone level (exclusive) to
          build the feature pyramid. Default: -1, which means the last level.
      add_extra_convs (bool): It decides whether to add conv
          layers on top of the original feature maps. Default to False.
          If True, its actual mode is specified by `extra_convs_on_inputs`.
      conv_cfg (dict): dictionary to construct and config conv layer.
      norm_cfg (dict): dictionary to construct and config norm layer.
      init_cfg (dict or list[dict], optional): Initialization config dict.
          Default: None
docstr: Initialize the weights of module.
docstr_tok: ["Initialize","the","weights","of","module","."]
code: def init_weights(self):
      """Initialize the weights of module."""
      super(NASFCOS_FPN, self).init_weights()
      for module in self.fpn.values():
          if hasattr(module, "conv_out"):
              caffe2_xavier_init(module.out_conv.conv)
  
      for modules in [
          self.adapt_convs.modules(),
          self.extra_downsamples.modules(),
      ]:
          for module in modules:
              if isinstance(module, nn.Conv2d):
                  caffe2_xavier_init(module)
  
file_path: mmdetection/mmdet/models/necks/nasfcos_fpn.py
imports: import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule
  from mmcv.ops.merge_cells import ConcatCell
  from mmengine.model import BaseModule, caffe2_xavier_init
  from mmdet.registry import MODELS
label_desc: Use image node_id 3 for calling the NASFCOS_FPN obj's underlying member method code with example usage: obj.init_weights() without return types